Guest Seminar by Sergio Bordel Cancer Systems Biology: Prediction of anti-cancer drug targets using Genome Scale Metabolic Models
Speaker: Sergio Bordel, Senior Researcher
Affiliations: Lithuanian University of Health Sciences, Lithuania and Universidad de Valladolid, Spain
Sergio Bordel holds a PhD in Process Engineering from the University of Valladolid (Spain), where he also obtained a Bachelor’s degree in Chemical Engineering. His main fields of expertise are systems and computational biology, bioinformatics, and non-equilibrium thermodynamics, which has resulted in the publication of 38 ISI papers and 4 book chapters.
Genome Scale Metabolic Models (GSMMs) are among the most promising tools to advance towards the goal of a holistic understanding of cell physiology. A cell’s networks contain three hierarchical levels of information, which are the metabolic reactions carried out in a particular cell type and the metabolites involved in these reactions, the enzymes that catalyze these reactions, and the genes coding these enzymes. The stoichiometry of the reactions in a metabolic network, combined with the condition of quasi-steady state of its internal metabolites, constrain the metabolic behavior of the cell and determine its capabilities. These models were originally introduced as tools for the metabolic engineering of industrially relevant microorganisms, however they have also a great (and still largely unexplored) potential for the understanding of human health.
Seminar will focus on how integrating RNA-seq data with a human GSMM can be used to identify personalized sets of metabolic targets and how GSMMs can be used to guide drug design. A python library pyTARG (https://github.com/